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Jupyter Scala is a Scala kernel for Jupyter. It aims at being a versatile and easily extensible alternative to other Scala kernels or notebook UIs, building on both Jupyter and Ammonite. The current version is available for Scala 2.11. Support for Scala 2.10 could be added back, and 2.12 should be supported soon (via ammonium / Ammonite).

Sparkmagic is a set of tools for interactively working with remote Spark clusters through Livy, a Spark REST server, in Jupyter notebooks. The Sparkmagic project includes a set of magics for interactively running Spark code in multiple languages, as well as some kernels that you can use to turn Jupyter into an integrated Spark environment. There are two ways to use sparkmagic. Head over to the examples section for a demonstration on how to use both models of execution.

A Jupyter kernel for Clojure. This will let you run Clojure code from the Jupyter console and notebook. This will install a clojupyter executable and a configuration file to tell Jupyter how to use clojupyter in from jupyter's user kernel location ( ~/.local/share/jupyter/kernels on linux and ~/Library/Jupyter/kernels on Mac).

This is a collection of IPython notebook/Jupyter notebooks intended to train the reader on different Apache Spark concepts, from basic to advanced, by using the Python language. If Python is not your language, and it is R, you may want to have a look at our R on Apache Spark (SparkR) notebooks instead. Additionally, if your are interested in being introduced to some basic Data Science Engineering, you might find these series of tutorials interesting. There we explain different concepts and applications using Python and R.

BeakerX is a collection of JVM kernels and interactive widgets for plotting, tables, autotranslation, and other extensions to Jupyter Notebook. BeakerX is in beta and under active development. The documentation consists of tutorial notebooks on GitHub. You can try it in the cloud for free with Binder. And here is the cheatsheet.

This is a completely dark theme for the Jupyter Notebook interface. Jupyter includes iPython 4 as its default kernel (which, confusingly, supports both Python 2.x and 3.x). Since the iPython 3 to 4 transition, it has gained better support for other interpreters like R and Ruby. It is possible to upgrade iPython 2 or 3 to Jupyter + iPython 4. Source code coloring is based on the Twilight theme for Textmate. Print preview output for notebooks retains a white background with printable foreground colors.

Do you use Vim? And you need to use Jupyter Notebook? This is a Jupyter Notebook (formerly known as IPython Notebook) extension to enable Vim like environment powered by CodeMirror's Vim. I'm sure that this plugin helps to improve your QOL. While I changed my job, I don't use jupyter notebook and I can't make enough time to maintain this plugin.

This is the F# implementation for Jupyter. View the Feature Notebook for some of the features that are included.You can use Jupyter F# Notebooks for free (with free server-side execution) at Azure Notebooks. If you select "Show me some samples", then there is an "Introduction to F#" which guides you through the language and its use in Jupyter.

xeus is a library meant to facilitate the implementation of kernels for Jupyter. It takes the burden of implementing the Jupyter Kernel protocol so developers can focus on implementing the interpreter part of the kernel. An example of kernel built with xeus is xeus-cling, a kernel for the C++ programming language based on the cling C++ interpreter.

The dashboards layout extension is an add-on for Jupyter Notebook. It lets you arrange your notebook outputs (text, plots, widgets, ...) in grid- or report-like layouts. It saves information about your layouts in your notebook document. Other people with the extension can open your notebook and view your layouts. For a sample of what's possible with the dashboard layout extension, have a look at the demo dashboard-notebooks in this repository.

While I love my job as a researcher, it doesn't exactly bring home the bacon. I mean.. it brings home some bacon... but like... not enough bacon? Right. Anyway, a colleague suggested I add an optional donation badge so users can help support projects like jupyter-themes (and the forthcoming lab-themes which will give users similar control over the look and feel of Jupyter Lab. Currently in early stages of development). I firmly believe that software is best served open and, as such, am committed to providing free and easy access to all my code. So if you can't make a financial contribution, then don't and pip install it anyway! But if you're sitting on some extra cash and enjoy using a package I've developed, then any amount helps and I greatly appreciate it.

This repository contains lecture transcripts and homework assignments as Jupyter Notebooks for the first of three Kadenze Academy courses on Creative Applications of Deep Learning w/ Tensorflow. It also contains a python package containing all the code developed during all three courses. The first course makes heavy usage of Jupyter Notebook. This will be necessary for submitting the homeworks and interacting with the guided session notebooks I will provide for each assignment. Follow along this guide and we'll see how to obtain all of the necessary libraries that we'll be using. By the end of this, you'll have installed Jupyter Notebook, NumPy, SciPy, and Matplotlib. While many of these libraries aren't necessary for performing the Deep Learning which we'll get to in later lectures, they are incredibly useful for manipulating data on your computer, preparing data for learning, and exploring results.

This video series will teach you how to solve machine learning problems using Python's popular scikit-learn library. It was featured on Kaggle's blog in 2015. There are 9 video tutorials totaling 4 hours, each with a corresponding Jupyter notebook. The notebook contains everything you see in the video: code, output, images, and comments.

An extensible environment for interactive and reproducible computing, based on the Jupyter Notebook and Architecture. Currently ready for users. JupyterLab is the next-generation user interface for Project Jupyter. It offers all the familiar building blocks of the classic Jupyter Notebook (notebook, terminal, text editor, file browser, rich outputs, etc.) in a flexible and powerful user interface. Eventually, JupyterLab will replace the classic Jupyter Notebook.

These series of tutorials on Data Science engineering will try to compare how different concepts in the discipline can be implemented in the two dominant ecosystems nowadays: R and Python. We will do this from a neutral point of view. Our opinion is that each environment has good and bad things, and any data scientist should know how to use both in order to be as prepared as posible for job market or to start personal project.

Jupyter Docker Stacks are a set of ready-to-run Docker images containing Jupyter applications and interactive computing tools. The two examples below may help you get started if you have Docker installed know which Docker image you want to use, and want to launch a single Jupyter Notebook server in a container.